Data-driven based Fault Diagnosis using Principal Component Analysis

Modern industrial systems are growing day by day and unlikely their complexity is also increasing. On the other hand, the design and operations have become a key focus of the researchers in order to improve the production system. To cope up with these chellenges, the data-driven technique like principal component analysis (PCA) is famous to assist the working systems. A data in bulk quanitity from the sensor measurements are often available in such industrial systems. Considering the modern industrial systems and their economic benifits, the fault diagnostic techniqes have been deeply studied. For example, the techniques that consider the process data as the key element. In this paper, the faults have been detected with the data-driven approach using PCA. In particular, the faults have been detected by using T^2 and Q statistics. In this process, PCA projects large data into smaller dimensions. Additionally it also preserves all the important information of process. In order to understand the impact of the technique, Tennessee Eastman chemical plant is considerd for the performance evaluation.

@article{Shaikh2018,
  title     = {Data-driven based Fault Diagnosis using Principal Component Analysis},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {9},
  number    = {7},
  year      = {2018},
  publisher = {The Science and Information Organization},
  author    = {Shakir M. Shaikh and Imtiaz A. Halepoto and Nazar H. Phulpoto and Muhammad S. Memon and Ayaz Hussain and Asif A. Laghari},
  doi       = {10.14569/IJACSA.2018.090725},
  url       = {https://doi.org/10.14569/IJACSA.2018.090725}
}